ContextualAI / ctxl-rerank-v2-instruct-multilingual-6b-nvfp4

Contextual AI Reranker v2 6B-NVFP4

Highlights

Our reranker is on the cost/performance Pareto frontier across 5 key areas:

• Instruction following (including capability to rank more recent information higher)
• Question answering
• Multilinguality
• Product search / recommendation systems
• Real-world use cases

For more details on these and other benchmarks, please refer to our blogpost .

Overview

• Model Type: Text Reranking
• Supported Languages: 100+
• Number of Paramaters: 6B
• Quantization: NVFP4
• Context Length: up to 32K
• Blogpost: https://contextual.ai/blog/rerank-v2

Quickstart

vLLM usage

Requires vllm==0.10.0 for NVFP4 or vllm>=0.8.5 for BF16.

import os
os.environ['VLLM_USE_V1'] = '0'  # v1 engine doesn’t support logits processor yet

import torch
from vllm import LLM, SamplingParams


def logits_processor(_, scores):
    """Custom logits processor for vLLM reranking."""
    index = scores[0].view(torch.uint16)
    scores = torch.full_like(scores, float("-inf"))
    scores[index] = 1
    return scores


def format_prompts(query: str, instruction: str, documents: list[str]) -> list[str]:
    """Format query and documents into prompts for reranking."""
    if instruction:
        instruction = f" {instruction}"
    prompts = []
    for doc in documents:
        prompt = f"Check whether a given document contains information helpful to answer the query.\n<Document> {doc}\n<Query> {query}{instruction} ??"
        prompts.append(prompt)
    return prompts


def infer_w_vllm(model_path: str, query: str, instruction: str, documents: list[str]):
    model = LLM(
        model=model_path,
        gpu_memory_utilization=0.85,
        max_model_len=8192,
        dtype="bfloat16",
        max_logprobs=2,
        max_num_batched_tokens=262144,
    )
    sampling_params = SamplingParams(
        temperature=0,
        max_tokens=1,
        logits_processors=[logits_processor]
    )
    prompts = format_prompts(query, instruction, documents)

    outputs = model.generate(prompts, sampling_params, use_tqdm=False)

    # Extract scores and create results
    results = []
    for i, output in enumerate(outputs):
        score = (
            torch.tensor([output.outputs[0].token_ids[0]], dtype=torch.uint16)
            .view(torch.bfloat16)
            .item()
        )    
        results.append((score, i, documents[i]))

    # Sort by score (descending)
    results = sorted(results, key=lambda x: x[0], reverse=True)

    print(f"Query: {query}")
    print(f"Instruction: {instruction}")
    for score, doc_id, doc in results:
        print(f"Score: {score:.4f} | Doc: {doc}")

Transformers Usage

Requires transformers>=4.51.0 for BF16. Not supported for NVFP4.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM


def format_prompts(query: str, instruction: str, documents: list[str]) -> list[str]:
    """Format query and documents into prompts for reranking."""
    if instruction:
        instruction = f" {instruction}"
    prompts = []
    for doc in documents:
        prompt = f"Check whether a given document contains information helpful to answer the query.\n<Document> {doc}\n<Query> {query}{instruction} ??"
        prompts.append(prompt)
    return prompts


def infer_w_hf(model_path: str, query: str, instruction: str, documents: list[str]):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32

    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side = "left"  # so -1 is the real last token for all prompts

    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype).to(device)
    model.eval()

    prompts = format_prompts(query, instruction, documents)
    enc = tokenizer(
        prompts,
        return_tensors="pt",
        padding=True,
        truncation=True,
    )
    input_ids = enc["input_ids"].to(device)
    attention_mask = enc["attention_mask"].to(device)

    with torch.no_grad():
        out = model(input_ids=input_ids, attention_mask=attention_mask)

    next_logits = out.logits[:, -1, :]  # [batch, vocab]

    scores_bf16 = next_logits[:, 0].to(torch.bfloat16)
    scores = scores_bf16.float().tolist()

    # Sort by score (descending)
    results = sorted([(s, i, documents[i]) for i, s in enumerate(scores)], key=lambda x: x[0], reverse=True)

    print(f"Query: {query}")
    print(f"Instruction: {instruction}")
    for score, doc_id, doc in results:
        print(f"Score: {score:.4f} | Doc: {doc}")

Citation

If you use this model, please cite:

@misc{ctxl_rerank_v2_instruct_multilingual,
      title={Contextual AI Reranker v2}, 
      author={George Halal, Sheshansh Agrawal, Bo Han, Arnav Palkhiwala},
      year={2025},
      url={https://contextual.ai/blog/rerank-v2}, 
}

License

Creative Commons Attribution Non Commercial Share Alike 4.0 (cc-by-nc-sa-4.0)

Contact

For questions or issues, please open an issue on the model repository or contact [email protected] .